''' Social Media Engagement Metrics Synthetic Dataset Generation'''
np.random.seed(42) #for considering same values everytime
# Number of records
num_records = 2000
# Generate synthetic data for Social Media User Engagement Metrics
data = {
'user_id': np.arange(1, num_records + 1), #'user_id': User ID, a unique identifier for each user.
'post_type': np.random.choice(['text', 'image', 'video'], num_records), #'post_type': Type of post ('text', 'image', 'video').
'post_length': np.random.randint(50, 300, num_records), #'post_length': Length of the post in characters.
'likes': np.random.randint(0, 500, num_records), #'likes': Number of likes the post received.
'comments': np.random.randint(0, 100, num_records),#'comments': Number of comments the post received.
'shares': np.random.randint(0, 50, num_records), #'shares': Number of shares the post received.
'engagement_rate': np.random.uniform(0.5, 5.0, num_records), #'engagement_rate': Overall engagement rate for the post.
'user_followers': np.random.randint(100, 10000, num_records), #'user_followers': Number of followers the user has.
'post_category': np.random.choice(['sports', 'technology', 'fashion', 'food'], num_records), #'post_category': Category of the post ('sports', 'technology', 'fashion', 'food').
'post_hour': np.random.randint(0, 24, num_records),#'post_hour': Hour of the day when the post was made.
'is_weekend': np.random.choice([0, 1], num_records),#'is_weekend': Binary indicator if the post was made on the weekend (1) or not (0).
'user_verified': np.random.choice([0, 1], num_records),#'user_verified': Binary indicator if the user is verified (1) or not (0).
'spam_flag': np.random.choice([0, 1], num_records)#'spam_flag': Binary indicator if the post is flagged as spam (1) or not (0).
}
# Create DataFrame
df = pd.DataFrame(data)
# Introduce NaN values for some records
nan_indices = np.random.choice(df.index, size=int(0.05 * len(df)), replace=False)
df.loc[nan_indices, 'post_length'] = np.nan
df.loc[nan_indices, 'engagement_rate'] = np.nan
# Display the first few rows of the generated dataset
print(df.head())
# Save the dataset to a CSV file
df.to_csv('social_media_engagement_metrics_dataset.csv', index=False)
